Overview

Dataset statistics

Number of variables10
Number of observations117564
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 MiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Mean_Integrated is highly overall correlated with SD and 3 other fieldsHigh correlation
SD is highly overall correlated with Mean_Integrated and 3 other fieldsHigh correlation
EK is highly overall correlated with Mean_Integrated and 3 other fieldsHigh correlation
Skewness is highly overall correlated with Mean_Integrated and 3 other fieldsHigh correlation
Mean_DMSNR_Curve is highly overall correlated with SD_DMSNR_Curve and 3 other fieldsHigh correlation
SD_DMSNR_Curve is highly overall correlated with Mean_DMSNR_Curve and 3 other fieldsHigh correlation
EK_DMSNR_Curve is highly overall correlated with Mean_DMSNR_Curve and 3 other fieldsHigh correlation
Skewness_DMSNR_Curve is highly overall correlated with Mean_DMSNR_Curve and 2 other fieldsHigh correlation
Class is highly overall correlated with Mean_Integrated and 6 other fieldsHigh correlation
Class is highly imbalanced (55.3%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-10-08 12:17:25.649884
Analysis finished2023-10-08 12:17:39.540413
Duration13.89 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct117564
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58781.5
Minimum0
Maximum117563
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size918.6 KiB
2023-10-08T12:17:39.662446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5878.15
Q129390.75
median58781.5
Q388172.25
95-th percentile111684.85
Maximum117563
Range117563
Interquartile range (IQR)58781.5

Descriptive statistics

Standard deviation33937.948
Coefficient of variation (CV)0.57735764
Kurtosis-1.2
Mean58781.5
Median Absolute Deviation (MAD)29391
Skewness0
Sum6.9105883 × 109
Variance1.1517843 × 109
MonotonicityStrictly increasing
2023-10-08T12:17:39.900097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
78385 1
 
< 0.1%
78383 1
 
< 0.1%
78382 1
 
< 0.1%
78381 1
 
< 0.1%
78380 1
 
< 0.1%
78379 1
 
< 0.1%
78378 1
 
< 0.1%
78377 1
 
< 0.1%
78376 1
 
< 0.1%
Other values (117554) 117554
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
117563 1
< 0.1%
117562 1
< 0.1%
117561 1
< 0.1%
117560 1
< 0.1%
117559 1
< 0.1%
117558 1
< 0.1%
117557 1
< 0.1%
117556 1
< 0.1%
117555 1
< 0.1%
117554 1
< 0.1%

Mean_Integrated
Real number (ℝ)

Distinct11065
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.2483
Minimum6.0546875
Maximum189.36719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size918.6 KiB
2023-10-08T12:17:40.125650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.0546875
5-th percentile48.726562
Q1104.54688
median116.66406
Q3126.29688
95-th percentile138.10156
Maximum189.36719
Range183.3125
Interquartile range (IQR)21.75

Descriptive statistics

Standard deviation24.906474
Coefficient of variation (CV)0.22388184
Kurtosis3.8966332
Mean111.2483
Median Absolute Deviation (MAD)10.75
Skewness-1.8413478
Sum13078795
Variance620.33244
MonotonicityNot monotonic
2023-10-08T12:17:40.362389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.7109375 127
 
0.1%
120.828125 121
 
0.1%
123.828125 113
 
0.1%
119.3046875 111
 
0.1%
114.8125 103
 
0.1%
110.828125 103
 
0.1%
114.8359375 99
 
0.1%
113.0703125 98
 
0.1%
114.0703125 96
 
0.1%
112.9140625 96
 
0.1%
Other values (11055) 116497
99.1%
ValueCountFrequency (%)
6.0546875 1
 
< 0.1%
6.109375 1
 
< 0.1%
6.1875 5
< 0.1%
6.234375 1
 
< 0.1%
6.265625 3
< 0.1%
6.375 1
 
< 0.1%
6.4140625 1
 
< 0.1%
6.5 1
 
< 0.1%
6.8515625 1
 
< 0.1%
6.921875 1
 
< 0.1%
ValueCountFrequency (%)
189.3671875 1
 
< 0.1%
186.9375 1
 
< 0.1%
184.828125 1
 
< 0.1%
184.4609375 5
< 0.1%
184.4140625 1
 
< 0.1%
183.453125 2
 
< 0.1%
181.140625 1
 
< 0.1%
180.21875 1
 
< 0.1%
177.5 3
< 0.1%
177.3359375 2
 
< 0.1%

SD
Real number (ℝ)

Distinct49262
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.713535
Minimum24.783273
Maximum93.602933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size918.6 KiB
2023-10-08T12:17:40.590327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.783273
5-th percentile34.83562
Q143.44339
median47.478932
Q350.862718
95-th percentile55.262267
Maximum93.602933
Range68.819661
Interquartile range (IQR)7.4193278

Descriptive statistics

Standard deviation6.1029406
Coefficient of variation (CV)0.13064609
Kurtosis0.7558716
Mean46.713535
Median Absolute Deviation (MAD)3.6530963
Skewness-0.52387943
Sum5491830.1
Variance37.245884
MonotonicityNot monotonic
2023-10-08T12:17:40.815514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.82915896 73
 
0.1%
45.82762157 59
 
0.1%
54.94868878 57
 
< 0.1%
44.95531636 54
 
< 0.1%
46.93619963 53
 
< 0.1%
48.9195413 52
 
< 0.1%
47.34355146 52
 
< 0.1%
48.91219013 51
 
< 0.1%
47.957271 50
 
< 0.1%
44.81400091 49
 
< 0.1%
Other values (49252) 117014
99.5%
ValueCountFrequency (%)
24.78327291 1
 
< 0.1%
25.06651792 2
< 0.1%
25.22005568 1
 
< 0.1%
25.62494676 1
 
< 0.1%
25.64761052 1
 
< 0.1%
25.67403438 1
 
< 0.1%
25.69524955 3
< 0.1%
25.86598272 1
 
< 0.1%
25.8865135 1
 
< 0.1%
26.06087466 1
 
< 0.1%
ValueCountFrequency (%)
93.60293344 1
< 0.1%
90.8525407 1
< 0.1%
86.50484652 1
< 0.1%
85.32084974 1
< 0.1%
83.91832635 1
< 0.1%
81.85308889 2
< 0.1%
81.73671477 1
< 0.1%
78.16734751 1
< 0.1%
78.08614947 1
< 0.1%
77.1397863 1
< 0.1%

EK
Real number (ℝ)

Distinct37132
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50349791
Minimum-1.7307817
Maximum7.8796277
Zeros0
Zeros (%)0.0%
Negative20555
Negative (%)17.5%
Memory size918.6 KiB
2023-10-08T12:17:41.055051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.7307817
5-th percentile-0.1450714
Q10.049760629
median0.18649811
Q30.39561956
95-th percentile3.4658905
Maximum7.8796277
Range9.6104094
Interquartile range (IQR)0.34585893

Descriptive statistics

Standard deviation1.127093
Coefficient of variation (CV)2.2385256
Kurtosis11.749821
Mean0.50349791
Median Absolute Deviation (MAD)0.16688949
Skewness3.4349969
Sum59193.229
Variance1.2703386
MonotonicityNot monotonic
2023-10-08T12:17:41.284655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.527957554 80
 
0.1%
0.262599764 68
 
0.1%
-0.049364179 67
 
0.1%
0.034810744 61
 
0.1%
0.339964064 57
 
< 0.1%
0.078459969 56
 
< 0.1%
4.838103704 54
 
< 0.1%
0.058494385 53
 
< 0.1%
0.095738664 53
 
< 0.1%
0.247988903 52
 
< 0.1%
Other values (37122) 116963
99.5%
ValueCountFrequency (%)
-1.730781724 1
< 0.1%
-1.707789078 1
< 0.1%
-1.679039339 2
< 0.1%
-1.64151544 1
< 0.1%
-1.633922495 1
< 0.1%
-1.624269471 1
< 0.1%
-1.623129658 1
< 0.1%
-1.58125809 2
< 0.1%
-1.539332047 1
< 0.1%
-1.478231 1
< 0.1%
ValueCountFrequency (%)
7.879627678 1
< 0.1%
7.860003087 1
< 0.1%
7.752495479 1
< 0.1%
7.70263554 1
< 0.1%
7.697826014 1
< 0.1%
7.627830786 1
< 0.1%
7.627580248 1
< 0.1%
7.623828606 1
< 0.1%
7.583681145 1
< 0.1%
7.572576517 1
< 0.1%

Skewness
Real number (ℝ)

Distinct41366
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8863853
Minimum-1.791886
Maximum65.385974
Zeros0
Zeros (%)0.0%
Negative49961
Negative (%)42.5%
Memory size918.6 KiB
2023-10-08T12:17:41.498381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.791886
5-th percentile-0.52063903
Q1-0.18895579
median0.091720478
Q30.69161283
95-th percentile14.439634
Maximum65.385974
Range67.17786
Interquartile range (IQR)0.88056862

Descriptive statistics

Standard deviation6.5154656
Coefficient of variation (CV)3.453942
Kurtosis20.577028
Mean1.8863853
Median Absolute Deviation (MAD)0.364716
Skewness4.3975754
Sum221771
Variance42.451292
MonotonicityNot monotonic
2023-10-08T12:17:41.729334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.374934811 83
 
0.1%
0.099693988 59
 
0.1%
-0.07976327 59
 
0.1%
0.039792179 58
 
< 0.1%
-0.253865826 50
 
< 0.1%
-0.098377946 49
 
< 0.1%
0.764473291 49
 
< 0.1%
-0.095198469 48
 
< 0.1%
0.948994138 47
 
< 0.1%
-0.264776811 47
 
< 0.1%
Other values (41356) 117015
99.5%
ValueCountFrequency (%)
-1.791885981 1
< 0.1%
-1.781888301 2
< 0.1%
-1.660049111 1
< 0.1%
-1.648617371 1
< 0.1%
-1.644425134 1
< 0.1%
-1.644414315 1
< 0.1%
-1.584885607 1
< 0.1%
-1.557604319 1
< 0.1%
-1.549020253 1
< 0.1%
-1.515920685 1
< 0.1%
ValueCountFrequency (%)
65.38597385 1
< 0.1%
63.26373147 1
< 0.1%
62.81463149 1
< 0.1%
61.99791594 1
< 0.1%
60.47768587 1
< 0.1%
58.05843453 1
< 0.1%
57.52006828 1
< 0.1%
57.50455774 1
< 0.1%
57.17523165 1
< 0.1%
57.16083048 1
< 0.1%

Mean_DMSNR_Curve
Real number (ℝ)

Distinct12474
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.962921
Minimum0.2132107
Maximum217.37124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size918.6 KiB
2023-10-08T12:17:41.958225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2132107
5-th percentile1.264214
Q12.090301
median2.8085284
Q34.1229097
95-th percentile78.407567
Maximum217.37124
Range217.15803
Interquartile range (IQR)2.0326087

Descriptive statistics

Standard deviation26.719946
Coefficient of variation (CV)2.2335637
Kurtosis12.154401
Mean11.962921
Median Absolute Deviation (MAD)0.8687291
Skewness3.4247096
Sum1406408.8
Variance713.95554
MonotonicityNot monotonic
2023-10-08T12:17:42.181853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.294314381 225
 
0.2%
2.33277592 224
 
0.2%
2.970735786 223
 
0.2%
1.940635452 220
 
0.2%
2.52090301 206
 
0.2%
2.943143813 192
 
0.2%
2.567725753 189
 
0.2%
2.717391304 181
 
0.2%
2.9590301 175
 
0.1%
2.816053512 169
 
0.1%
Other values (12464) 115560
98.3%
ValueCountFrequency (%)
0.213210702 6
< 0.1%
0.2909699 2
 
< 0.1%
0.31270903 2
 
< 0.1%
0.323578595 2
 
< 0.1%
0.341973244 1
 
< 0.1%
0.352842809 1
 
< 0.1%
0.367056856 1
 
< 0.1%
0.367892977 1
 
< 0.1%
0.369565217 1
 
< 0.1%
0.372073579 1
 
< 0.1%
ValueCountFrequency (%)
217.3712375 2
< 0.1%
209.3001672 1
 
< 0.1%
208.6295987 2
< 0.1%
207.3026756 1
 
< 0.1%
203.8177258 1
 
< 0.1%
202.3319398 2
< 0.1%
200.458194 1
 
< 0.1%
199.5777592 4
< 0.1%
199.5351171 1
 
< 0.1%
199.3135452 2
< 0.1%

SD_DMSNR_Curve
Real number (ℝ)

Distinct48050
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.190678
Minimum7.3704322
Maximum109.89078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size918.6 KiB
2023-10-08T12:17:42.408567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.3704322
5-th percentile11.883665
Q114.955405
median18.164924
Q324.732218
95-th percentile76.924867
Maximum109.89078
Range102.52035
Interquartile range (IQR)9.776813

Descriptive statistics

Standard deviation20.041937
Coefficient of variation (CV)0.76523168
Kurtosis2.9645146
Mean26.190678
Median Absolute Deviation (MAD)3.9350514
Skewness2.0103449
Sum3079080.9
Variance401.67923
MonotonicityNot monotonic
2023-10-08T12:17:42.633264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.76626465 65
 
0.1%
14.83555934 63
 
0.1%
13.82601301 57
 
< 0.1%
17.89844422 56
 
< 0.1%
14.72370745 50
 
< 0.1%
17.91224533 47
 
< 0.1%
15.86849195 47
 
< 0.1%
15.92900231 47
 
< 0.1%
18.63421079 45
 
< 0.1%
18.81215941 45
 
< 0.1%
Other values (48040) 117042
99.6%
ValueCountFrequency (%)
7.370432165 3
< 0.1%
7.473461921 1
 
< 0.1%
7.663910248 1
 
< 0.1%
7.664622639 1
 
< 0.1%
7.804608673 1
 
< 0.1%
7.946781424 2
< 0.1%
7.957518897 1
 
< 0.1%
7.994774769 1
 
< 0.1%
8.024076816 1
 
< 0.1%
8.026750758 1
 
< 0.1%
ValueCountFrequency (%)
109.8907849 1
 
< 0.1%
109.7126491 2
< 0.1%
109.6909087 1
 
< 0.1%
109.5757343 1
 
< 0.1%
109.5619535 1
 
< 0.1%
109.2511708 1
 
< 0.1%
108.9314268 2
< 0.1%
108.9131219 1
 
< 0.1%
108.9018796 3
< 0.1%
108.8275081 1
 
< 0.1%

EK_DMSNR_Curve
Real number (ℝ)

Distinct42870
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0374883
Minimum-2.5978719
Maximum34.539844
Zeros0
Zeros (%)0.0%
Negative3409
Negative (%)2.9%
Memory size918.6 KiB
2023-10-08T12:17:42.855479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.5978719
5-th percentile0.59493038
Q16.7429112
median8.4428828
Q310.003237
95-th percentile13.703689
Maximum34.539844
Range37.137716
Interquartile range (IQR)3.2603261

Descriptive statistics

Standard deviation3.8409802
Coefficient of variation (CV)0.47788315
Kurtosis1.3698607
Mean8.0374883
Median Absolute Deviation (MAD)1.6379396
Skewness-0.041686179
Sum944919.28
Variance14.753129
MonotonicityNot monotonic
2023-10-08T12:17:43.094437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.96401612 73
 
0.1%
8.43257251 64
 
0.1%
10.63844438 61
 
0.1%
8.89029666 59
 
0.1%
8.858354116 59
 
0.1%
8.979295493 58
 
< 0.1%
8.039443285 56
 
< 0.1%
7.849797678 56
 
< 0.1%
9.019420736 54
 
< 0.1%
8.235864174 52
 
< 0.1%
Other values (42860) 116972
99.5%
ValueCountFrequency (%)
-2.597871861 1
< 0.1%
-2.550922925 1
< 0.1%
-2.545733541 2
< 0.1%
-2.495522074 1
< 0.1%
-2.473625985 1
< 0.1%
-2.47361387 1
< 0.1%
-2.470787703 1
< 0.1%
-2.462512109 1
< 0.1%
-2.449008501 2
< 0.1%
-2.392788155 1
< 0.1%
ValueCountFrequency (%)
34.53984419 3
< 0.1%
32.17418904 1
 
< 0.1%
31.31226734 1
 
< 0.1%
30.99291931 1
 
< 0.1%
29.89709964 1
 
< 0.1%
28.98802871 1
 
< 0.1%
28.29723073 2
< 0.1%
28.13728243 1
 
< 0.1%
28.05250136 1
 
< 0.1%
27.96806604 2
< 0.1%

Skewness_DMSNR_Curve
Real number (ℝ)

Distinct50648
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.881076
Minimum-1.9769756
Maximum1191.0008
Zeros0
Zeros (%)0.0%
Negative8597
Negative (%)7.3%
Memory size918.6 KiB
2023-10-08T12:17:43.331949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.9769756
5-th percentile-0.78511577
Q149.409136
median83.421375
Q3122.09329
95-th percentile228.00129
Maximum1191.0008
Range1192.9778
Interquartile range (IQR)72.684154

Descriptive statistics

Standard deviation79.96211
Coefficient of variation (CV)0.85173833
Kurtosis11.350114
Mean93.881076
Median Absolute Deviation (MAD)36.162412
Skewness2.3740334
Sum11037035
Variance6393.9391
MonotonicityNot monotonic
2023-10-08T12:17:43.568448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.914087857 85
 
0.1%
72.82662621 54
 
< 0.1%
81.8654112 49
 
< 0.1%
77.86910097 44
 
< 0.1%
-1.928211959 43
 
< 0.1%
52.92907564 41
 
< 0.1%
118.908113 41
 
< 0.1%
65.08793777 41
 
< 0.1%
98.81337916 40
 
< 0.1%
-0.918212092 40
 
< 0.1%
Other values (50638) 117086
99.6%
ValueCountFrequency (%)
-1.976975603 2
 
< 0.1%
-1.973761069 1
 
< 0.1%
-1.972978508 1
 
< 0.1%
-1.966926392 2
 
< 0.1%
-1.966926228 1
 
< 0.1%
-1.964997899 5
< 0.1%
-1.964320987 1
 
< 0.1%
-1.949108868 3
 
< 0.1%
-1.948954964 12
< 0.1%
-1.947999318 1
 
< 0.1%
ValueCountFrequency (%)
1191.000837 3
< 0.1%
1072.793069 1
 
< 0.1%
1022.201175 1
 
< 0.1%
1017.403028 1
 
< 0.1%
949.7002887 1
 
< 0.1%
900.3674111 1
 
< 0.1%
900.094444 1
 
< 0.1%
881.7167208 1
 
< 0.1%
880.1561192 1
 
< 0.1%
863.4332292 2
< 0.1%

Class
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size918.6 KiB
0
106597 
1
10967 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters117564
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106597
90.7%
1 10967
 
9.3%

Length

2023-10-08T12:17:43.771886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-08T12:17:43.923415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 106597
90.7%
1 10967
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 106597
90.7%
1 10967
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117564
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106597
90.7%
1 10967
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 117564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106597
90.7%
1 10967
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106597
90.7%
1 10967
 
9.3%

Interactions

2023-10-08T12:17:37.070928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:27.595556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:28.846094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:30.573497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:31.685744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:32.749053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:33.823377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:34.872468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:35.938155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:37.193272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:27.761070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:28.970799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:30.697786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:31.809385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:32.877008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:33.945800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:34.990272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:36.063188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:37.320021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:27.937256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:29.096984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:30.826257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:31.932040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:33.004308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:34.062515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:35.117018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:36.191205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:37.444790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:28.104673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:29.826554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:30.947140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:32.049370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:33.124650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:34.175202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:35.232041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:36.317654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:37.563231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:28.220645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:29.949796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:31.062534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:32.159160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:33.238611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:34.290995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:35.347172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:36.439466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:37.686605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:28.344314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:30.079470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:31.184048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:32.280253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:33.354522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:34.406626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:35.464864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:36.570788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:37.803370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:28.456569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:30.197593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:31.302845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:32.392295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:33.465963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:34.514849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:35.580354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:36.687664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:37.928984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:28.571250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:30.314930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:31.420609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:32.505624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:33.577940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:34.628212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:35.687083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:36.805092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:38.104660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:28.698939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:30.447379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:31.553892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:32.627958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:33.700787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:34.751588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:35.813922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-08T12:17:36.935633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-08T12:17:44.047059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idMean_IntegratedSDEKSkewnessMean_DMSNR_CurveSD_DMSNR_CurveEK_DMSNR_CurveSkewness_DMSNR_CurveClass
id1.000-0.005-0.0040.0070.0040.0020.002-0.001-0.0010.003
Mean_Integrated-0.0051.0000.537-0.874-0.653-0.127-0.1340.1290.1290.903
SD-0.0040.5371.000-0.549-0.899-0.073-0.0800.0750.0760.650
EK0.007-0.874-0.5491.0000.6820.1260.134-0.127-0.1280.940
Skewness0.004-0.653-0.8990.6821.0000.1090.116-0.111-0.1110.893
Mean_DMSNR_Curve0.002-0.127-0.0730.1260.1091.0000.933-0.988-0.9830.596
SD_DMSNR_Curve0.002-0.134-0.0800.1340.1160.9331.000-0.925-0.9610.720
EK_DMSNR_Curve-0.0010.1290.075-0.127-0.111-0.988-0.9251.0000.9910.637
Skewness_DMSNR_Curve-0.0010.1290.076-0.128-0.111-0.983-0.9610.9911.0000.190
Class0.0030.9030.6500.9400.8930.5960.7200.6370.1901.000

Missing values

2023-10-08T12:17:38.954143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-08T12:17:39.288360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idMean_IntegratedSDEKSkewnessMean_DMSNR_CurveSD_DMSNR_CurveEK_DMSNR_CurveSkewness_DMSNR_CurveClass
00133.17187559.7160810.043133-0.70338354.91722470.0844380.749798-0.6495120
1187.09375036.2579730.4354692.2660573.41722421.8650697.03933052.6862510
22112.64062539.8183930.3796390.9223062.73076915.6896908.19347185.6497850
33120.67968845.918448-0.0984900.0117752.69648820.9546628.18387470.3328990
44134.07031257.720107-0.107772-0.5733351.10786011.25505116.107748308.7537650
55131.63281252.563210-0.075253-0.4958252.19481615.5374259.03343997.0324060
66110.93750041.5569550.3128440.5590221.96571917.19146910.396774118.7242700
77120.20312549.927902-0.089990-0.3213673.28010018.3768408.19056177.9172370
88112.41406246.9398660.2825510.1517843.33695721.9295297.69333065.1862790
9999.85937548.0891890.6937100.2816633.41471624.1819107.95868465.0845750
idMean_IntegratedSDEKSkewnessMean_DMSNR_CurveSD_DMSNR_CurveEK_DMSNR_CurveSkewness_DMSNR_CurveClass
117554117554105.35156245.0540300.3169090.1858412.56939816.0498648.51831888.6750550
11755511755534.98437527.4610345.47313332.74425846.02508468.8017231.5166250.7455291
117556117556149.28906251.407380-0.6699910.14775644.52090379.3257011.253041-0.4867880
117557117557125.17187551.9825320.247792-0.3948741.45819411.40418513.343828235.3043360
11755811755852.00000033.0961763.86955921.814703122.99331172.944748-0.115862-0.9240211
117559117559132.84375056.748838-0.060070-0.5540844.05434827.8441446.56442344.4426640
117560117560112.57812552.5392710.179580-0.3069611.63796015.33191311.384718142.5354700
117561117561119.75781249.9800130.064402-0.2708223.87792619.7885596.95974056.3677890
117562117562105.78906246.9865950.4414260.3724662.09782617.1706129.44244599.0745390
117563117563113.51562550.9359560.031517-0.44383123.29097062.0068422.3863644.0018590